7 papers · 1 filter
Adaptive Distance-Aware Trunk Deep Operator Learning for Long-Span Roadway Bridges
Bilal Ahmed, Diab W. Abueidda, Waleed El-Sekelly +2
Long-span roadway bridges exhibit highly localized structural responses under vehicular loading, making repeated FE analysis computationally expensive for applications such as infl…
Single vs. Multiple Branches in DeepONet and S-DeepONet: Network Architecture Follows Coupling in Multiphysics Systems
Jaewan Park, Kazuma Kobayashi, Qibang Liu +3
`Real-time prediction of complex physical systems requires surrogate models that learn from data while representing strong multiphysics coupling. Deep Operator Networks have shown…
Geometry-Informed Neural Operator Transformer
Qibang Liu, Weiheng Zhong, Hadi Meidani +3
Machine-learning-based surrogate models offer significant computational efficiency and faster simulations compared to traditional numerical methods, especially for problems requiri…
From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences
Kazuma Kobayashi, Samrendra Roy, Seid Koric +2
Accurate reconstruction of latent environmental fields from sparse and indirect observations is a foundational challenge across scientific domains-from atmospheric science and geop…
Physics-informed Multiple-Input Operators for efficient dynamic response prediction of structures
Bilal Ahmed, Yuqing Qiu, Diab W. Abueidda +3
Finite element (FE) modeling is essential for structural analysis but remains computationally intensive, especially under dynamic loading. While operator learning models have shown…
Virtual Sensing-Enabled Digital Twin Framework for Real-Time Monitoring of Nuclear Systems Leveraging Deep Neural Operators
Raisa Bentay Hossain, Farid Ahmed, Kazuma Kobayashi +3
Effective real-time monitoring is a foundation of digital twin technology, crucial for detecting material degradation and maintaining the structural integrity of nuclear systems to…